1. Field of the Invention
The invention concerns a method for the online adaptation of a characteristic of a hybrid vehicle used to select an operating mode and/or to determine an operating point of the drive train.
2. Description of the Related Art
Steadily rising fuel costs as well as stricter legal regulations regarding vehicle emissions place ever higher requirements on the efficiency of motor vehicles. A great potential for reducing fuel consumption is represented by hybridization of the drive train. In doing this, the drive train is extended by at least one electrical machine and an associated electrical storage system.
Depending on the implementation of the hybridization, besides a purely combustion engine operation there can thus be further different operating modes, such as electric drive, boost mode, brake recovery, load point shifting, etc. An operating mode is characterized by the combination of a specific component configuration and the form of the energy that flows in the participating components of a drive train.
The different operating modes of a hybridized drive train must be used purposefully during the operation of the vehicle to achieve the maximum possible reduction of fuel consumption. During this, further aims or target criteria shall also be observed, because the increase in the fuel efficiency always takes place in the area of conflict between emission minimization, component protection, and/or ride comfort. It is therefore necessary to select, within the context of a higher level operating strategy, an optimal operating mode regarding the predetermined target criteria, with a corresponding optimal power distribution in the drive train, from a predetermined number of possible operating modes of the hybridized drive train for the current operating point in time.
Different techniques and methods are known from the prior art for the selection of a respective optimal operating mode within the context of an operating strategy. By way of example, reference is made for this to the following publications from the prior art:    [1]: A. Wilde, A modular functional architecture for adaptive and predictive energy management in hybrid vehicles, dissertation, TU München, 2009; and    [2]: J. von Grundherr, Derivation of a heuristic operating strategy for a hybrid vehicle from online optimization, dissertation, TU München, 2010; and    [3]: Onori et al., Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles, Proceedings of the ASME 2010, Dynamic Systems and Control Conference DSCC2010 Sep. 12-15, 2010, Cambridge (Mass.), USA.
For example, methods based on so-called online optimizations are known that consider the vehicle to be a multi-dimensional optimizing problem that is described by a target function that not only includes physical variables such as the consumption, but also other requirements such as the noise emissions or the lifetime protection of components, etc. Thus for example, an overall cost function is proposed in the publication [2], by which, besides the target criterion “fuel efficiency”, further target criteria such as for example the driveability (ride comfort) are taken into account. During this, target cost functions corresponding to the individual target criteria are combined to form an overall cost function by a summation, and then the decision regarding the operating strategy is calculated from the overall cost function.
For each predetermined target criteria, at least one assessment variable for the quantitative description of the respective target criterion is established or determined in advance. For the calculation of the assessment variables and for modelling the cost functions, as a rule, a plurality of characteristics is used.
Above all, with many target criteria that have to take into account by an operating strategy, the application of an operating mode selection method is complex due to the many characteristics with numerous dependencies.
Further, the application of an operating strategy is, as a rule, only carried out for a single planned use of the vehicle. In most cases however, deviations occur between the planned and the actual use of the vehicle. It would therefore be desirable to recognize said deviations during the operation of the vehicle and to adapt the operating strategy accordingly. Using such deviation data, the characteristics of the operating strategy could be adapted online, i.e. could be dynamically adapted while the vehicle is in driving mode, which with a rising dependency of the characteristics on each other is correspondingly difficult to achieve and/or requires high computing complexity. Further, in the operating strategy predictive information can be processed to respond to altered boundary conditions that precondition the energy storage arrangement for future events, and thus ensure the maximum target achievement. This too requires the online adaptation of the characteristics.